Enormous resources continue to be expended toward advances in image capture and processing to find better ways of increasing image resolution. However, there remains a significant technological gap between theoretical optical resolution limits and current sensor resolutions, particularly for short wavelengths (e.g., 380-400 nm). This is due in part to the need in device fabrication technology to increase the number of devices in a given space and then route signals to and from these devices using lead lines, both of which are becoming increasingly problematic. Thus, digital sensor resolution is limited by the physical size of the photosensing elements (or pixels) that can be manufactured.
A computational technique called super-resolution can be used to increase the resolution beyond the physical limit. Super-resolution can produce images of a higher resolution than the resolution of the originally captured image. One category of super-resolution algorithms includes reconstruction-based algorithms (RBAs) which are the most commonly-used algorithms for super-resolution. RBAs model the process of image formation to build a relation between a low-resolution image (LRI) and a high-resolution image (HRI). RBAs rely on the relationship between the LRIs and the HRI, and assume various kinds of prior conditions on the HRI in order to regularize the framework in preparation for the complex and ill-posed inverse problem.
The RBA usually first forms a linear systemL=PH+E, where L is the column vector of the irradiance of all low-resolution pixels (LRPs) considered, H is the vector of the irradiance of the HRI, P gives the weights of the high-resolution pixels (HRPs) in order to obtain the irradiance of the corresponding LRPs, and E is the noise. In all previous work, the LRPs appear on the left-hand side of the equation and the LRIs are all rectangular regular grids with square pixels, the regular layout. Based on such a configuration, both practice and theoretical analysis have shown that the magnification factor is limited to a relatively small number.
Moreover, conventional research in super-resolution has raised significant doubts regarding the usability of RBAs in super-resolution in the real world for the regular layout. For example, when the magnification factor becomes large, performance of the RBA deteriorates. Accordingly, different approaches to resolution enhancement should be taken in order to overcome these limitations.